Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)
| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 6/2/2025 | Comida | 52730 | Andrés | supermercado (no cobre el otro de 25k pq muchas son cosas mías) |
| 9/2/2025 | Comida | 12500 | Andrés | NA |
| 17/2/2025 | Comida | 7940 | Andrés | NA |
| 18/2/2025 | Electricidad | 64888 | Andrés | la puse por adelantado para que no se me olvide |
| 18/2/2025 | Comida | 17820 | Tami | Supermercado |
| 23/2/2025 | Comida | 86908 | Tami | Supermercado |
| 27/2/2025 | Comida | 10000 | Andrés | NA |
| 26/2/2025 | Comida | 4620 | Andrés | NA |
| 1/3/2025 | Comida | 2300 | Tami | Supermercado |
| 2/3/2025 | Comida | 102058 | Tami | Supermercado |
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 17/3/2025 | Electricidad | 52458 | Andrés | NA |
| 17/3/2025 | VTR | 22000 | Andrés | NA |
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot)
plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2)
tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F)
print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0045e+09 2 5.1314 0.0061 **
## lag_depvar 2.6291e+11 1 2686.0325 <2e-16 ***
## Residuals 8.1239e+10 830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1850.888 16308.56 0.1482974
## 2-0 31382.375 23210.244 39554.51 0.0000000
## 2-1 24153.537 19421.498 28885.58 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
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## 776 40161.71 2 36232.14
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## 778 45663.71 2 40629.86
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## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
## 825 52624.86 2 43416.57
## 826 57733.71 2 52624.86
## 827 54120.57 2 57733.71
## 828 53353.43 2 54120.57
## 829 56286.86 2 53353.43
## 830 60626.86 2 56286.86
## 831 61375.29 2 60626.86
## 832 53710.86 2 61375.29
## 833 55795.57 2 53710.86
## 834 55130.14 2 55795.57
## 835 57700.14 2 55130.14
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 678 53616.64 22168.457
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2020.05516 4040.87288 -538.53416 2437.67080 -2970.54590 518.41377
## 8 9 10 11 12 13
## -5656.33688 -1187.14601 -3965.40373 -416.55848 -4938.49517 -1607.34513
## 14 15 16 17 18 19
## -897.68446 379.40369 -3241.33937 -375.91823 -2128.35372 6606.06236
## 20 21 22 23 24 25
## -1529.22511 -1208.11190 1475.91541 -1186.80388 234.61603 1694.81652
## 26 27 28 29 30 31
## -7102.68099 948.59248 8193.11250 417.06114 -15.05712 -2401.48857
## 32 33 34 35 36 37
## 1575.96012 4571.96293 1125.46528 2389.97263 -1869.76752 4606.84261
## 38 39 40 41 42 43
## 4303.11757 -2276.90145 -2981.94535 -1109.90649 -10740.98683 7292.15840
## 44 45 46 47 48 49
## 2558.04161 1366.94756 8104.94412 684.15104 6526.87232 6711.30301
## 50 51 52 53 54 55
## -5886.51542 -4797.74343 -5060.63966 -7928.20356 6131.92839 -4076.15234
## 56 57 58 59 60 61
## -4893.01505 3858.04044 888.88417 -31.42322 142.81606 -4995.96742
## 62 63 64 65 66 67
## 18128.30244 3637.89073 -3649.32316 5923.43578 7341.10452 14634.78250
## 68 69 70 71 72 73
## 1686.47819 -13218.81389 -1308.26885 4642.08632 -4902.48880 -4405.20142
## 74 75 76 77 78 79
## -10496.85168 2470.15533 -5397.24561 1067.49228 -6863.11120 551.56687
## 80 81 82 83 84 85
## -2350.50336 -2689.56249 -3927.27601 -532.33182 2319.41557 3766.33281
## 86 87 88 89 90 91
## 479.09705 -482.79602 198.23009 4303.26811 -1163.00593 1151.14571
## 92 93 94 95 96 97
## -2064.52315 -1043.79794 178.29356 275.35725 -7483.61029 2394.22466
## 98 99 100 101 102 103
## -8600.90703 -2936.62641 -4034.93015 -1731.33689 -1255.84474 3186.36387
## 104 105 106 107 108 109
## -2338.10017 2598.22681 -1155.40381 973.46576 2589.48700 -3153.00362
## 110 111 112 113 114 115
## -4720.90655 -846.96631 1906.73253 11695.87578 -1243.84817 2667.81494
## 116 117 118 119 120 121
## 4261.52283 3500.40937 -1102.81242 -4718.42698 -3724.48755 2320.59590
## 122 123 124 125 126 127
## -1732.47526 1341.17183 8858.62214 845.03650 128.50177 -2522.90852
## 128 129 130 131 132 133
## 2654.43543 7051.31659 1009.49901 -8502.17579 1749.22750 4135.10417
## 134 135 136 137 138 139
## -3165.41618 -1420.06384 -853.80447 -3879.54736 1184.61882 -494.36584
## 140 141 142 143 144 145
## -2912.42826 1720.07996 -1879.83801 -7827.63379 2043.19130 -3476.99563
## 146 147 148 149 150 151
## 2105.60588 -255.13692 1025.12439 -357.78334 1353.60355 1187.45896
## 152 153 154 155 156 157
## 3356.94465 -4862.43701 -1173.63990 -3234.73234 5958.60309 9746.59027
## 158 159 160 161 162 163
## -3657.43879 -5003.11408 3380.36155 -30.45822 2470.42599 -6137.78638
## 164 165 166 167 168 169
## -6973.02943 3932.99003 17166.11175 3386.57600 -642.28849 -2689.16177
## 170 171 172 173 174 175
## -1345.41094 3350.00381 -471.31191 -8318.26136 2626.31836 4085.43271
## 176 177 178 179 180 181
## 382.02640 8506.38569 -9498.94264 -3714.91379 -10985.50902 -11473.89183
## 182 183 184 185 186 187
## 1007.75758 9063.14182 -1669.00335 5689.58831 6309.74399 12904.95017
## 188 189 190 191 192 193
## 8163.03369 -4340.79712 2189.66054 10089.68432 -1934.08023 -2733.12090
## 194 195 196 197 198 199
## -10566.16156 -6637.52059 966.28799 -5501.70422 -10058.38397 5131.99535
## 200 201 202 203 204 205
## -3325.67667 -1966.45954 -1056.64183 6242.01928 9619.71569 301.26459
## 206 207 208 209 210 211
## 2646.30569 2815.91356 5499.00281 12542.16513 -5992.93206 -11591.37483
## 212 213 214 215 216 217
## -5944.90867 -10857.26261 -5330.87764 1277.47559 -13262.16011 16153.52515
## 218 219 220 221 222 223
## 7550.31661 1257.65336 26415.36468 12216.26855 7010.00183 13695.59737
## 224 225 226 227 228 229
## -4258.92571 -2074.65969 3451.86572 35.32125 2427.45969 8689.02897
## 230 231 232 233 234 235
## 5510.94857 -2223.98143 -2136.03455 9125.68993 -11815.84231 -7571.80984
## 236 237 238 239 240 241
## -8817.92604 -10366.27492 2825.64650 1095.56730 -8555.57361 -9237.56760
## 242 243 244 245 246 247
## 8857.14213 -8017.06584 2244.70164 -10549.02057 -4289.79177 1189.39016
## 248 249 250 251 252 253
## 764.53533 -12558.72493 3414.61049 1824.64363 3967.56666 1881.46511
## 254 255 256 257 258 259
## -1419.29845 10880.13503 20602.58925 2883.42849 -4588.94355 3800.59456
## 260 261 262 263 264 265
## -2008.24817 3428.60203 -5164.09947 -11195.70088 -5011.61695 -796.93338
## 266 267 268 269 270 271
## -5463.17860 8510.65942 -4564.91643 3911.04204 -2394.12013 4146.40965
## 272 273 274 275 276 277
## 415.52159 7007.61697 -1721.48116 11718.52620 -4914.53364 1404.66475
## 278 279 280 281 282 283
## -695.34768 7530.55031 -5392.54930 -3052.78632 -11574.34509 -2955.80998
## 284 285 286 287 288 289
## 18374.53074 7462.61069 2397.98492 -967.85461 571.35469 6064.52499
## 290 291 292 293 294 295
## 6537.35320 -19128.89564 -11444.70345 -8396.64685 9410.98104 2794.52810
## 296 297 298 299 300 301
## -1463.49139 27121.22458 9716.51817 4531.89829 9143.84672 2466.52890
## 302 303 304 305 306 307
## -1419.98055 7521.26708 -24681.60631 -3845.61435 -471.09698 -7259.25340
## 308 309 310 311 312 313
## -4240.17801 2676.88497 -9454.30989 -3464.95811 -8411.86634 1362.10525
## 314 315 316 317 318 319
## -3361.82863 1843.31498 -4297.71267 27237.48243 -1035.45340 2983.23469
## 320 321 322 323 324 325
## 10514.39740 5244.37132 32025.33298 4671.34634 -21373.35078 1434.72246
## 326 327 328 329 330 331
## 757.16133 -6812.32492 -2053.52990 -33575.18318 715.66567 -2470.95892
## 332 333 334 335 336 337
## -255.03485 -3330.92940 3930.34533 -608.83610 -7125.37162 -3269.37884
## 338 339 340 341 342 343
## -2338.32564 -7823.63956 3727.49932 -1515.67157 -1883.62043 -1139.65364
## 344 345 346 347 348 349
## 28.44111 327.14866 -1780.23106 -9608.31228 -13346.12148 2210.04720
## 350 351 352 353 354 355
## -4441.71886 -3771.32108 -6089.69635 1651.45305 1267.43695 2620.57862
## 356 357 358 359 360 361
## -3918.89482 -664.07503 522.86836 6848.81971 79.34166 -240.72522
## 362 363 364 365 366 367
## 2377.20003 -2968.33037 -1086.07013 -8949.70477 -4799.39871 -6370.88477
## 368 369 370 371 372 373
## -5088.36623 -7378.54711 4908.93604 234.56000 6973.20242 -7818.17342
## 374 375 376 377 378 379
## -2418.96192 -3540.21882 -2611.48111 -12598.25559 1803.40524 -10751.67747
## 380 381 382 383 384 385
## 5610.07164 9214.52715 2956.65817 -2587.55839 1420.13536 6547.79032
## 386 387 388 389 390 391
## 11182.36876 -6080.38691 -5620.06011 -395.41845 8323.90488 1538.89266
## 392 393 394 395 396 397
## 10938.76161 -10206.72349 2488.09401 416.66173 265.96941 -949.71339
## 398 399 400 401 402 403
## -853.38556 -14772.46683 8305.75247 -1429.55158 -1612.77584 6749.64271
## 404 405 406 407 408 409
## -8192.80359 -1519.17653 -2745.46858 -6019.66552 -3032.75911 -4079.16091
## 410 411 412 413 414 415
## -8902.66850 6019.47465 1491.71062 -7535.47804 -7827.28365 14112.29229
## 416 417 418 419 420 421
## 3632.65359 4283.56861 -8269.33870 -4942.71697 -2779.40258 2651.17660
## 422 423 424 425 426 427
## -14195.14289 -2916.88308 -9218.34494 2925.66682 6865.55027 6422.63709
## 428 429 430 431 432 433
## -4176.75992 -4295.85799 -4882.67455 -1933.89124 -5854.15106 -6750.42505
## 434 435 436 437 438 439
## -6052.76841 -1480.75128 -940.70961 -5075.56061 2491.81622 4726.53192
## 440 441 442 443 444 445
## -5200.83336 -2287.67729 1448.22943 -3980.12542 2702.30596 -6731.05615
## 446 447 448 449 450 451
## -12241.04643 -4598.41010 9566.15943 -2162.65601 4625.43941 -6025.11291
## 452 453 454 455 456 457
## -1258.14244 247.11621 2882.13293 -12430.33442 3253.37614 -6838.47936
## 458 459 460 461 462 463
## 6406.23269 2861.99032 2339.67281 -4026.58570 1925.70720 -185.87971
## 464 465 466 467 468 469
## 1612.45613 -710.85907 3161.86535 -2842.37646 5613.64994 -7156.81576
## 470 471 472 473 474 475
## -3147.36688 -2375.31264 -4824.97032 2852.87325 7638.30272 -6209.88950
## 476 477 478 479 480 481
## 1316.63740 -6353.42005 -2994.58599 1870.61105 -13082.74669 -9860.78531
## 482 483 484 485 486 487
## -1278.14590 -61.54745 -1054.26203 -1439.48901 -9685.97857 11021.94016
## 488 489 490 491 492 493
## 6108.12012 7262.87341 -5626.14180 5198.68275 9101.48885 5826.13554
## 494 495 496 497 498 499
## -13721.46644 -10755.27298 -3588.00496 -1241.69613 -659.39373 -7762.63749
## 500 501 502 503 504 505
## 500.50551 4169.15801 5368.48990 495.61230 -87.94885 -7408.58319
## 506 507 508 509 510 511
## 427.57809 -5195.69867 1701.94149 -1438.01518 -8297.63278 -714.47487
## 512 513 514 515 516 517
## -2790.57730 -699.47594 1216.46643 -9621.84314 -7861.47628 24210.92452
## 518 519 520 521 522 523
## 9648.07736 5663.97178 -5570.90313 2587.72399 16800.08957 11192.53428
## 524 525 526 527 528 529
## -24465.38136 -5272.17634 -3923.20373 4397.58807 -549.34875 -11293.07948
## 530 531 532 533 534 535
## 4244.22257 13742.76615 -5198.29273 4173.92175 5339.24402 -2025.22706
## 536 537 538 539 540 541
## -4764.64975 -7276.50899 -2273.31265 8157.94193 -69.96901 -8337.37297
## 542 543 544 545 546 547
## 1652.91523 -770.56212 197.16726 -11201.96404 -11191.28219 1948.60545
## 548 549 550 551 552 553
## 6895.73687 -1458.45064 697.56910 -7866.82488 8445.01572 748.89094
## 554 555 556 557 558 559
## -12107.72188 9041.87148 8495.99547 -98.77900 4655.25307 -3790.30468
## 560 561 562 563 564 565
## 13908.28514 21245.05248 -6797.68545 -9974.56943 6528.09155 -41.05323
## 566 567 568 569 570 571
## 3194.16322 -7643.64898 -17537.19999 6507.08623 6254.04316 1699.22067
## 572 573 574 575 576 577
## 2891.70799 1555.59354 -2381.42600 14513.51432 -9905.79915 -6458.25520
## 578 579 580 581 582 583
## 8524.67826 2640.09471 -6770.54621 7313.44358 -4021.28657 -2977.95019
## 584 585 586 587 588 589
## 15513.90449 -14745.50667 8242.42840 -145.38862 -6423.78452 -937.53041
## 590 591 592 593 594 595
## 73.74270 -10830.61138 1661.01557 -7288.98718 2948.32002 8731.74378
## 596 597 598 599 600 601
## -7671.10665 5708.93489 2568.31409 6678.76583 -3391.80367 5962.99873
## 602 603 604 605 606 607
## -8505.57268 2083.12500 1090.68099 2956.15384 1303.71130 203.59753
## 608 609 610 611 612 613
## -6002.31145 7907.70461 -1378.99739 -2760.90175 -3627.02770 -8386.81279
## 614 615 616 617 618 619
## 11831.00422 4763.81311 -9502.70949 11475.14723 5857.49316 -5782.57536
## 620 621 622 623 624 625
## 26176.61424 -13095.22259 -6989.91080 2978.29698 -4345.14345 -10759.20081
## 626 627 628 629 630 631
## 11168.59966 -21803.46710 -2507.93230 8585.20721 11011.09167 -1716.37092
## 632 633 634 635 636 637
## 33127.63710 -6853.09503 5485.92443 5158.13735 -2517.14987 -5574.86310
## 638 639 640 641 642 643
## -2142.58664 -12620.57179 -2386.08158 -2022.27063 -2650.60873 -2981.08930
## 644 645 646 647 648 649
## 1699.50661 4306.77113 16824.31567 18356.47666 630.56585 4543.50303
## 650 651 652 653 654 655
## 10360.48417 19876.07273 423.38712 -28364.95301 -1534.30332 -2471.02254
## 656 657 658 659 660 661
## 1703.55931 -3355.80353 -10769.50876 1552.89064 4109.84917 -1135.22331
## 662 663 664 665 666 667
## 12908.19951 1199.54421 1653.34484 -11851.78086 1255.92818 1061.45750
## 668 669 670 671 672 673
## -5293.45924 -7521.03175 1976.93837 -3808.47613 2585.56558 -3476.14407
## 674 675 676 677 678 679
## -9426.24523 -8374.75559 -3032.45095 116.90358 2783.13642 635.90795
## 680 681 682 683 684 685
## -3910.13816 -1886.47561 -1396.36165 -8321.82078 4584.56443 -2323.85018
## 686 687 688 689 690 691
## -1478.42947 506.41930 10767.18532 9734.42227 10486.28719 -9819.81049
## 692 693 694 695 696 697
## -3680.38189 -3254.66524 5764.82342 -10504.28497 -8004.29661 -8687.35224
## 698 699 700 701 702 703
## -6335.20212 -4792.38421 3032.09715 -4465.48173 -1958.04817 4160.47691
## 704 705 706 707 708 709
## 31031.20112 9404.78848 23329.17729 1558.35447 8212.11125 22814.89754
## 710 711 712 713 714 715
## 6453.84720 -18300.40130 4748.48883 -5514.16572 -164.04792 418.63558
## 716 717 718 719 720 721
## -17326.30255 -5313.56504 3288.17486 -3062.20788 -13025.56804 4239.25990
## 722 723 724 725 726 727
## -5600.20131 700.64819 -3978.03897 -12489.44025 1331.01750 -1906.26047
## 728 729 730 731 732 733
## -9817.28500 17232.95020 1727.83234 -2771.63793 5667.55543 -8681.67311
## 734 735 736 737 738 739
## -769.70555 8091.86737 -15402.89074 -5953.95609 7367.32639 -4830.79871
## 740 741 742 743 744 745
## 116.45229 1782.30980 -2003.06187 -5214.73954 6368.13967 -6323.83539
## 746 747 748 749 750 751
## 22652.97428 7769.15180 -2007.60439 -7346.22778 23363.40768 -4354.11693
## 752 753 754 755 756 757
## 1338.24561 -14478.95636 56060.42262 26854.02857 15027.11712 -10711.04694
## 758 759 760 761 762 763
## 10552.57495 7261.56144 5758.66710 -46437.66224 -16201.60924 940.80723
## 764 765 766 767 768 769
## -2543.87422 -3483.83084 122818.53473 19267.69308 43678.88775 22543.55576
## 770 771 772 773 774 775
## 12035.87570 15809.12364 25690.49317 -98845.87936 -6781.28505 -35854.93255
## 776 777 778 779 780 781
## 1717.71601 -1248.92517 3375.73478 -7435.71869 -1465.29194 -1965.60008
## 782 783 784 785 786 787
## 3404.14602 -7175.84083 -2256.63147 3899.76637 2245.40140 -2778.93466
## 788 789 790 791 792 793
## -4066.65335 1720.30610 2834.88511 -69.89590 -6721.54279 -5799.80376
## 794 795 796 797 798 799
## -1164.12878 -1279.96333 -7834.14149 -2370.30952 -3284.67044 -2682.07413
## 800 801 802 803 804 805
## 10707.74009 2223.50116 7061.85598 2905.18348 -5466.44414 8170.26285
## 806 807 808 809 810 811
## 9856.95100 -10620.38780 -7412.74379 -7520.23838 2991.84884 4179.83419
## 812 813 814 815 816 817
## -2284.45379 -14179.38508 -4123.14184 6247.20967 8223.67196 -9687.66480
## 818 819 820 821 822 823
## -7762.96933 -9348.12968 9743.03682 -1235.73590 -4384.58493 -8459.29928
## 824 825 826 827 828 829
## 7863.30098 7901.04923 4961.07119 -3117.65303 -726.59769 2877.37984
## 830 831 832 833 834 835
## 4653.31050 1608.20511 -6710.41453 2073.67076 -413.97773 2737.66418
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17249.23 20098.13 24354.68 24072.47 26427.26 23758.30 24475.05 19704.29
## 10 11 12 13 14 15 16 17
## 19440.69 16781.84 17559.78 14287.20 14338.40 15003.45 16701.05 15020.06
## 18 19 20 21 22 23 24 25
## 16055.35 15428.51 22515.23 21598.68 21078.23 22969.38 22294.96 22947.90
## 26 27 28 29 30 31 32 33
## 24794.97 18719.69 20446.89 28288.94 28346.63 28019.35 25647.33 27050.61
## 34 35 36 37 38 39 40 41
## 30895.96 31244.60 32654.62 30163.73 34139.88 37349.90 34404.23 31213.19
## 42 43 44 45 46 47 48 49
## 30060.27 20634.13 28157.39 30595.34 31685.20 38527.42 38021.70 42686.70
## 50 51 52 53 54 55 56 57
## 46925.52 39619.03 34184.21 29203.92 22344.21 28638.01 25216.59 21511.96
## 58 59 60 61 62 63 64 65
## 25922.97 27183.28 27480.47 27892.54 23760.98 40362.25 42207.32 37450.42
## 66 67 68 69 70 71 72 73
## 41659.90 46578.50 57253.09 55265.67 40499.98 38004.34 41024.06 35320.77
## 74 75 76 77 78 79 80 81
## 30770.28 21468.13 24671.53 20594.79 22682.11 17574.58 19591.22 18817.28
## 82 83 84 85 86 87 88 89
## 17844.42 15912.19 17190.73 20800.95 25221.33 26211.80 26236.77 26853.87
## 90 91 92 93 94 95 96 97
## 30981.43 29811.28 30811.24 28874.51 28073.85 28442.21 28849.04 22422.63
## 98 99 100 101 102 103 104 105
## 25439.48 18465.77 17321.22 15360.77 15660.70 16338.49 20813.81 19896.77
## 106 107 108 109 110 111 112 113
## 23409.98 23199.82 24876.94 27755.43 25252.05 21693.39 21968.98 24616.84
## 114 115 116 117 118 119 120 121
## 35487.85 33679.61 35518.19 38518.30 40475.38 38162.43 32980.34 29319.55
## 122 123 124 125 126 127 128 129
## 31403.62 29682.54 30864.81 38469.11 38111.36 37172.34 34033.99 35816.25
## 130 131 132 133 134 135 136 137
## 41217.36 40657.32 31853.77 33119.32 36310.99 32719.49 31105.80 30190.26
## 138 139 140 141 142 143 144 145
## 26745.24 28160.51 27930.00 25614.92 27640.55 26264.49 19862.81 22895.14
## 146 147 148 149 150 151 152 153
## 20720.54 23699.42 24239.73 25831.07 26013.25 27668.40 28969.91 32003.87
## 154 155 156 157 158 159 160 161
## 27471.35 26733.88 24287.68 30185.27 41677.87 40007.11 37370.50 42393.74
## 162 163 164 165 166 167 168 169
## 43803.15 47221.07 42684.32 37988.72 43417.17 59729.00 61942.43 60355.59
## 170 171 172 173 174 175 176 177
## 57179.41 55577.71 58281.88 57305.40 49592.97 52418.14 56162.97 56199.19
## 178 179 180 181 182 183 184 185
## 63332.23 53828.91 50577.94 41381.18 32915.53 36425.86 46535.29 45990.98
## 186 187 188 189 190 191 192 193
## 51947.26 57695.62 68484.97 73770.94 67461.91 67655.46 74729.94 70403.84
## 194 195 196 197 198 199 200 201
## 65924.02 55161.52 49188.14 50613.28 46205.38 38369.58 44798.11 43024.46
## 202 203 204 205 206 207 208 209
## 42662.21 43140.84 49938.86 58833.31 58462.69 60188.52 61845.28 65638.69
## 210 211 212 213 214 215 216 217
## 75110.79 67188.95 55371.05 49976.69 40967.73 37923.67 41039.16 31053.47
## 218 219 220 221 222 223 224 225
## 48036.97 55362.06 56264.49 79043.30 86542.71 88547.12 96142.93 87088.52
## 226 227 228 229 230 231 232 233
## 81083.42 80665.11 77313.11 76474.11 81213.91 82578.98 77011.18 72221.31
## 234 235 236 237 238 239 240 241
## 77878.27 64518.24 56550.07 48495.99 40102.64 44297.00 46451.00 39897.85
## 242 243 244 245 246 247 248 249
## 33573.72 43862.21 38105.73 42043.73 34303.08 33008.18 36665.61 39491.15
## 250 251 252 253 254 255 256 257
## 30315.25 36256.78 40060.43 45258.25 47978.16 47470.44 57777.41 75284.86
## 258 259 260 261 262 263 264 265
## 75099.80 68406.55 69889.25 66107.83 67554.81 61308.84 50577.19 46602.22
## 266 267 268 269 270 271 272 273
## 46811.75 42916.20 51725.49 47996.39 52145.55 50261.02 54330.76 54626.95
## 274 275 276 277 278 279 280 281
## 60647.91 58280.76 67959.39 61880.62 62090.78 60438.88 66185.12 59911.93
## 282 283 284 285 286 287 288 289
## 56473.77 46019.95 44415.75 61658.10 67191.44 67601.14 65017.22 64104.05
## 290 291 292 293 294 295 296 297
## 68107.36 72019.90 53005.27 43101.50 37109.02 47436.47 50680.21 49793.63
## 298 299 300 301 302 303 304 305
## 74004.20 79953.10 80621.15 85236.33 83433.84 78461.16 81930.03 56814.04
## 306 307 308 309 310 311 312 313
## 53072.95 52752.54 46539.04 43746.83 47352.31 39900.10 38621.44 33179.75
## 314 315 316 317 318 319 320 321
## 36966.54 36147.40 39981.14 37964.37 63766.02 61605.91 63230.46 71233.34
## 322 323 324 325 326 327 328 329
## 73622.10 99118.94 97495.64 73311.42 72108.55 70464.90 62411.82 59532.33
## 330 331 332 333 334 335 336 337
## 29462.76 33152.53 33592.32 35913.64 35254.08 41024.55 42100.80 37345.52
## 338 339 340 341 342 343 344 345
## 36559.47 36686.21 32002.36 38004.96 38668.76 38927.37 39803.70 41590.71
## 346 347 348 349 350 351 352 353
## 43413.80 43165.31 36105.69 26667.81 32015.72 30876.04 30465.84 28080.83
## 354 355 356 357 358 359 360 361
## 32762.56 36519.14 40985.47 39173.36 40434.42 42574.18 49973.94 50524.87
## 362 363 364 365 366 367 368 369
## 50726.66 53191.33 50673.21 50117.42 42758.11 39953.17 36127.79 33905.12
## 370 371 372 373 374 375 376 377
## 29960.49 37252.87 39541.23 47431.60 41399.53 40846.36 39382.77 38915.26
## 378 379 380 381 382 383 384 385
## 29777.31 34378.25 27425.64 35650.04 45989.48 49557.13 47829.44 49822.35
## 386 387 388 389 390 391 392 393
## 56046.35 65537.67 58744.77 53209.56 52938.10 60322.25 60845.95 69520.01
## 394 395 396 397 398 399 400 401
## 58618.91 60186.77 59746.60 59230.14 57716.10 56476.90 43227.25 51818.27
## 402 403 404 405 406 407 408 409
## 50818.06 49783.64 56188.95 48726.75 48037.47 46363.09 42037.62 40867.59
## 410 411 412 413 414 415 416 417
## 38930.24 33020.67 40898.43 43826.62 38495.57 33580.71 48461.77 52309.00
## 418 419 420 421 422 423 424 425
## 56240.77 48705.15 45026.12 43701.25 47290.00 35701.74 35430.77 29685.90
## 426 427 428 429 430 431 432 433
## 35279.31 43612.22 50508.76 47272.14 44338.96 41262.18 41150.29 37625.85
## 434 435 436 437 438 439 440 441
## 33761.77 30994.04 32571.14 34421.70 32425.04 37294.33 43503.83 40254.11
## 442 443 444 445 446 447 448 449
## 39959.91 42968.27 40852.98 44845.06 40088.90 31115.41 29952.13 41316.37
## 450 451 452 453 454 455 456 457
## 40997.70 46652.54 42285.86 42635.74 44257.30 47977.91 37845.62 42698.05
## 458 459 460 461 462 463 464 465
## 38118.34 45692.30 49214.61 51836.87 48564.29 50906.59 51108.26 52856.43
## 466 467 468 469 470 471 472 473
## 52353.71 55299.38 52625.92 57680.39 50935.94 48545.31 47130.54 43752.70
## 474 475 476 477 478 479 480 481
## 47511.27 54979.46 49402.79 51107.13 45892.59 44270.53 47105.32 36512.64
## 482 483 484 485 486 487 488 489
## 30070.00 31940.55 34638.98 36129.92 37096.41 30733.06 43271.45 49935.98
## 490 491 492 493 494 495 496 497
## 56770.71 51478.75 56314.94 63953.58 67767.47 54014.84 44586.58 42610.27
## 498 499 500 501 502 503 504 505
## 42933.68 43725.35 38208.49 40608.98 45913.94 51599.24 52309.38 52420.01
## 506 507 508 509 510 511 512 513
## 46117.85 47458.70 43715.49 46472.73 46138.20 39849.90 40981.72 40156.33
## 514 515 516 517 518 519 520 521
## 41262.68 43904.41 36739.90 32016.22 55921.35 64087.31 67742.62 61117.42
## 522 523 524 525 526 527 528 529
## 62457.77 76052.18 83033.38 57967.46 52834.20 49526.41 53908.21 53414.22
## 530 531 532 533 534 535 536 537
## 43591.49 48586.52 61255.15 55772.51 59172.33 63162.66 60213.36 55240.94
## 538 539 540 541 542 543 544 545
## 48699.03 47354.06 55296.25 55046.52 47601.80 49826.85 49653.40 50347.68
## 546 547 548 549 550 551 552 553
## 40990.71 32821.25 37165.83 45287.59 45084.43 46791.40 40797.41 49816.11
## 554 555 556 557 558 559 560 561
## 50972.15 40744.84 50291.86 58159.64 57524.18 61124.16 56888.71 68656.66
## 562 563 564 565 566 567 568 569
## 85355.83 75440.57 63996.91 68418.91 66542.12 67729.51 59294.20 43273.20
## 570 571 572 573 574 575 576 577
## 50286.24 56195.07 57378.58 59455.41 60102.85 57227.49 69481.80 58848.54
## 578 579 580 581 582 583 584 585
## 52567.61 60173.91 61678.83 54768.56 61039.00 56612.38 53655.10 67233.65
## 586 587 588 589 590 591 592 593
## 52653.14 60001.96 59093.78 52812.10 52116.83 52393.04 43103.13 45901.70
## 594 595 596 597 598 599 600 601
## 40524.82 44773.26 53541.96 46869.07 52731.69 55110.95 60783.52 56939.29
## 602 603 604 605 606 607 608 609
## 61756.00 53319.45 55200.60 55977.42 58287.00 58861.40 58401.88 52575.72
## 610 611 612 613 614 615 616 617
## 59641.71 57700.62 54796.03 51500.10 44458.71 55976.04 59865.85 50795.71
## 618 619 620 621 622 623 624 625
## 61204.08 65391.58 58877.39 81118.51 66232.20 58556.85 60561.00 55911.49
## 626 627 628 629 630 631 632 633
## 46240.97 56954.90 37499.36 37359.51 46933.62 57422.66 55466.08 84212.52
## 634 635 636 637 638 639 640 641
## 74392.79 76594.86 78233.15 72956.29 65671.16 62303.43 50201.08 48568.41
## 642 643 644 645 646 647 648 649
## 47459.32 45940.66 44324.35 47002.80 51622.97 66602.81 81035.72 78157.35
## 650 651 652 653 654 655 656 657
## 79061.66 84936.64 98389.33 93144.81 63397.16 60847.45 57800.01 58785.23
## 658 659 660 661 662 663 664 665
## 55224.08 45631.11 48016.87 52337.22 51528.94 63097.60 62975.23 63264.92
## 666 667 668 669 670 671 672 673
## 51713.50 53073.83 54092.89 49428.89 43405.06 46441.76 44039.15 47528.00
## 674 675 676 677 678 679 680 681
## 45279.10 38112.47 32767.31 32764.81 35515.44 40250.23 42512.00 40515.33
## 682 683 684 685 686 687 688 689
## 40538.93 40987.96 35327.01 41660.14 41157.29 41456.72 43453.39 54167.43
## 690 691 692 693 694 695 696 697
## 62629.71 70683.67 59974.24 55979.67 52860.18 58017.28 48304.44 41999.78
## 698 699 700 701 702 703 704 705
## 35891.92 32609.10 31088.19 36598.05 34860.62 35533.67 41470.08 70146.35
## 706 707 708 709 710 711 712 713
## 76308.54 93865.93 90183.03 92779.82 107813.72 106653.69 84002.37 84349.88
## 714 715 716 717 718 719 720 721
## 75683.19 72784.22 70759.59 53479.28 48874.97 52369.07 49872.43 38981.31
## 722 723 724 725 726 727 728 729
## 44552.49 40821.64 43068.04 40942.01 31643.98 35596.97 36222.57 29854.48
## 730 731 732 733 734 735 736 737
## 47932.45 50181.35 48214.16 53871.24 46273.56 46548.28 54534.18 40978.10
## 738 739 740 741 742 743 744 745
## 37388.10 45894.08 42666.83 44170.26 46940.49 46053.17 42470.29 49462.98
## 746 747 748 749 750 751 752 753
## 44481.31 65455.13 70778.32 66885.51 58816.45 78606.26 71676.75 70595.38
## 754 755 756 757 758 759 760 761
## 55824.58 104571.11 121650.88 126242.33 107758.28 110187.87 109434.90 107463.09
## 762 763 764 765 766 767 768 769
## 60115.47 45158.48 47068.73 45692.55 43668.04 152297.59 156736.83 181954.59
## 770 771 772 773 774 775 776 777
## 185522.98 179457.45 177453.79 184339.59 81502.86 72087.08 38444.00 41878.78
## 778 779 780 781 782 783 784 785
## 42287.98 46688.00 41083.86 41404.03 41246.57 45802.56 40537.06 40234.38
## 786 787 788 789 790 791 792 793
## 45351.03 48377.36 46630.94 43978.84 46718.97 50088.32 50494.40 45035.23
## 794 795 796 797 798 799 800 801
## 41069.13 41654.39 42064.71 36694.45 36776.24 36048.50 35939.12 47547.36
## 802 803 804 805 806 807 808 809
## 50278.00 56893.96 59043.59 53605.02 60770.91 68508.82 57373.46 50443.95
## 810 811 812 813 814 815 816 817
## 44293.01 48105.02 52475.45 50645.24 38648.28 36951.93 44533.76 52888.52
## 818 819 820 821 822 823 824 825
## 44535.26 38916.13 32618.96 43802.02 43980.58 41384.30 35553.27 44723.81
## 826 827 828 829 830 831 832 833
## 52772.64 57238.22 54080.03 53409.48 55973.55 59767.08 60421.27 53721.90
## 834 835
## 55544.12 54962.48
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8105
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.131378 0.7657782 3.877284
## t2* 2686.032476 164.6887957 889.637081
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.148163 5.109093 13.3986
## 2 lag_depvar 1641.308991 2732.390646 4518.6302
Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)
## =-=-=-=-= Iteration 0 Mon Apr 21 00:55:40 2025
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## =-=-=-=-= Iteration 2000 Mon Apr 21 00:55:49 2025
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## =-=-=-=-= Iteration 4000 Mon Apr 21 00:55:59 2025
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## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Apr 21 00:56:27 2025
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## =-=-=-=-= Iteration 12000 Mon Apr 21 00:56:37 2025
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## =-=-=-=-=
#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 6.520667 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 316.617667 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 57.448667 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 4.396667 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 27.483333 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 30.833000 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 14.663333 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 457.963333 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2685, min uf: 26799.01, min date: 2018-01-01
#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
## Warning: Removed 2685 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
##
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
##
## invoke
## The following object is masked from 'package:data.table':
##
## :=
model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-05-09 00:04:58 sería de: 26.714 pesos// Percentil 95% más alto proyectado: 35.134,38
Según TimeGPT: La proyección de la UF a 298 días más 2026-03-03 sería de: 40.071,14 pesos// Percentil 80% más alto proyectado: 40.455,09 pesos// Percentil 95% más alto proyectado: 41.529,13
Según prophet: La proyección de la UF a 298 días más 2026-03-03 sería de: 40.248 pesos// Percentil 95% más alto proyectado: 44.723
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26328.94 | 26324.01 |
| Lo.80 | 26461.63 | 26488.42 |
| Point.Forecast | 26714.13 | 26799.01 |
| Hi.80 | 31516.13 | 32160.60 |
| Hi.95 | 34398.32 | 34998.86 |
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t
Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m
## ARIMA(1,1,1)
##
## Coefficients:
## ar1 ma1
## 0.3483 -0.9346
## s.e. 0.1374 0.0704
##
## sigma^2 = 38816: log likelihood = -488.95
## AIC=983.91 AICc=984.26 BIC=990.78
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.3520 564.4043 14.9274
## s.e. 0.1125 301.6200 9.2576
##
## sigma^2 = 37056: log likelihood = -492.78
## AIC=993.57 AICc=994.15 BIC=1002.78
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 674.8956 | 656.9731 | 697.1199 |
| Lo.80 | 814.4207 | 806.2363 | 793.4172 |
| Point.Forecast | 1077.9901 | 1089.9752 | 1012.6503 |
| Hi.80 | 1341.5594 | 1379.9564 | 1291.7041 |
| Hi.95 | 1481.0846 | 1533.4632 | 1468.9823 |
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.13
## [10] tidytext_0.4.2 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [16] xts_0.14.1 forecast_8.24.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.4
## [28] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.8.17 lattice_0.22-6
## [34] GGally_2.2.1 ggplot2_3.5.2 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.0 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [43] stringi_1.8.7 DataExplorer_0.8.3 data.table_1.17.0
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.0.2
## [4] httr2_1.1.2 lifecycle_1.0.4 StanHeaders_2.32.10
## [7] doParallel_1.0.17 globals_0.16.3 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.1.0 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.10 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] doRNG_1.8.6.2 askpass_1.2.1 pkgbuild_1.4.7
## [22] DBI_1.2.3 abind_1.4-8 quadprog_1.5-8
## [25] nnet_7.3-19 rappdirs_0.3.3 sandwich_3.1-1
## [28] inline_0.3.21 tokenizers_0.3.0 listenv_0.9.1
## [31] anytime_0.3.11 performance_0.13.0 spatial_7.3-17
## [34] parallelly_1.43.0 codetools_0.2-20 xml2_1.3.8
## [37] tidyselect_1.2.1 ggeffects_2.2.1 farver_2.1.2
## [40] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [43] stats4_4.4.0 jsonlite_2.0.0 ellipsis_0.3.2
## [46] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.2
## [49] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [52] xfun_0.52 TTR_0.24.4 ggfortify_0.4.17
## [55] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [58] fastmap_1.2.0 boot_1.3-30 openssl_2.3.2
## [61] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [64] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [67] networkD3_0.4 gtools_3.9.5 generics_0.1.3
## [70] htmlwidgets_1.6.4 ggstats_0.9.0 pkgconfig_2.0.3
## [73] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [76] selectr_0.4-2 janeaustenr_1.0.0 htmltools_0.5.8.1
## [79] carData_3.0-5 tseries_0.10-58 snakecase_0.11.1
## [82] knitr_1.50 rstudioapi_0.17.1 tzdb_0.5.0
## [85] uuid_1.2-1 nlme_3.1-164 curl_6.2.2
## [88] cachem_1.1.0 sjlabelled_1.2.0 KernSmooth_2.23-22
## [91] parallel_4.4.0 fBasics_4041.97 pillar_1.10.2
## [94] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [97] car_3.1-3 dbplyr_2.5.0 xtable_1.8-4
## [100] evaluate_1.0.3 mvtnorm_1.3-3 cli_3.6.4
## [103] compiler_4.4.0 crayon_1.5.3 rngtools_1.5.2
## [106] future.apply_1.11.3 labeling_0.4.3 sjmisc_2.8.10
## [109] rstan_2.32.7 QuickJSR_1.7.0 viridisLite_0.4.2
## [112] assertthat_0.2.1 munsell_0.5.1 lazyeval_0.2.2
## [115] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [118] bit64_4.6.0-1 future_1.40.0 nixtlar_0.6.2
## [121] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [124] bslib_0.9.0 quantmod_0.4.27 bit_4.6.0
#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))